Innovative Projects Realized

Mécanismes corticaux de récupération du mouvement

Environ deux tiers des blessures à la moelle épinière touchent la région cervicale, causant une tétraplégie. Au Canada, 40 000 personnes ont une fonction de bras et de main réduite à cause de ces blessures. Même si certains conservent un mouvement volontaire partiel, la récupération est souvent limitée. Les nouvelles technologies neuroprothétiques cherchent à améliorer la récupération motrice, avec des résultats prometteurs pour la stimulation de la moelle épinière et du nerf périphérique. Cependant, ces méthodes n’engagent pas optimalement tous les chemins du mouvement volontaire. La recherche récente se concentre sur la stimulation des régions motrices du cerveau, montrant des améliorations immédiates de la qualité de la démarche et du contrôle des jambes. Notre but est maintenant de comprendre le rôle des réseaux corticaux dans la récupération de la fonction de la main/du bras et de développer une neuroprothèse pour améliorer le mouvement et la récupération à long terme.

Optimizing hearing aids for speech intelligibility, sound quality and emotional speech

The lack of correspondence between the clinic setting and real world listening environments may explain, in part, why hearing instruments optimized in the clinic often result in poor adherence of hearing instruments in everyday life. To address the disconnect between communication in everyday listening situations where talkers often speak with emotion and audiology clinics where test speech materials consist of tokens recorded with a monotonic and/or neutral intonation, the proposed research will assess hearing impaired listeners with regard to speech understanding, emotion understanding, physiological response to emotional speech, and self reported experience of emotional speech. Different studies will focus on different aspects of the problem and with different
listening conditions (e.g., with hearing aids and without).

Harnessing the power of backscatter communication and multi-sensor data analytics for next generation smart building

In this proposal, we provide a smart building communication, configuration, and interaction solution. It includes: 1) a smart building network design with lightweight wireless nodes that can recycle ambient radio energy (visible light and electromagnetic waves) to save energy and do relays for each other as a large-scale robust interconnection; 2) an AI-empowered network node configuration without no human intervention for automatic smart building control; 3) a no-dead end interaction between human and in-building environmental data, through building-wide sensors/cameras and 360-degree online video.

Access to Psychologists and Social Workers and Workplace Absenteeism in Ontario

When workers experience poor mental health, it has significant costs for both individuals and the economy. It affects their overall well-being, income, and productivity (e.g., sickness absences). Workers with poor mental
health need access to adequate mental health services. Thus, this study examines the impact of seeing a psychologist or social worker for mental health reasons on workers’ absences in Ontario and looks at how this
differs between subgroups facing inequalities in accessing mental health support (e.g., males) and higher rates of workplace absences (e.g., hourly-wage earners). We will analyze data from a national survey on health and
apply robust health economics research approaches to estimate this relationship and how it differs by sociodemographic and occupation-related factors. By highlighting the potential for community-based mental
health providers to reduce absences among the workforce, this study can inform policy decisions on improving workplace mental health support and expanding public mental healthcare.

The Development of Chan and Pure Land Buddhism in the Middle Tang Dynasty

Looking back at the history of Buddhism in medieval China, more and more scholars believe that the Tang Dynasty was a significant turning point in the development of Buddhist schools. Before the mid-Tang Dynasty, various Buddhist sects developed well.However, the southern Chan and the Pure Land were the dominant sects after the mid-Tang Dynasty. Furthermore, “Chan and Pure Land dominance” has always been the norm in Chinese Buddhism after the Tang Dynasty. In this context, this study will mainly explore three questions: (1) What caused Southern Chan and Pure Land to stand out among the many Buddhist sects of the Tang Dynasty? (2) How to understand the relationship between Southern Chan and Pure Land that they unite with each other and attack each other? (3) How to understand the regional incongruity and regional difference between Southern Chan and Pure Land Development?

Avalanche awareness among snowshoers and winter hikers on the North Shore Mountains

Snowshoers and winter hikers have received little attention from academic research. As a growing community of backcountry users, it is not well understood to what extent they travel in avalanche terrain, what level of avalanche safety training they possess, and where they get their information for undertaking winter backcountry trips. This study addresses these concerns by designing and conducting a survey of snowshoers and winter hikers at a popular mountain resort just outside of Vancouver, B.C, Canada. The goal of this study is to analyze and understand the characteristics of snowshoers and winter hikers, to create better avalanche safety products and messaging, and to create safer experiences in the winter backcountry.

Creating a scalable model for optimizing terrestrial & mangrove tree counts and survival estimates using computer vision

In efforts to offset carbon emissions, many companies have recently invested in tree planting efforts; planted
trees absorb carbon dioxide from the atmosphere and store that carbon as they grow. The amount of carbon a
tree can store depends on a variety of factors, including tree size, species, and whether the tree is alive or dead.
In large scale tree-planting efforts, like those performed by Veritree, estimating carbon offset accurately requires
these factors to be recorded for a vast number of trees. This time-intensive process can be shortened by
developing “computer vision” algorithms which can automatically detect trees and extract their features from
smartphone images. Using computer vision, this project aims to increase the rate at which Veritree can survey
their tree populations, ultimately providing a more comprehensive assessment of their planting efforts.

Next Generation Fuels and Fuel Additives

There is a need to develop renewable fuels derived from biological sources that have chemical properties that are similar to petroleum-derived fuels. We are investigating the use
of chemical building blocks made by bacteria that may be used as fuel additives to improve the cold weather performance of biodiesel, or directly as fuels if chemically converted to
hydrocarbons. The bacteria of interest can synthesize natural polyester polymers called polyhydroxyalkaonates (PHAs) from low cost ag-industrial by-product (“waste”) streams.
Short chain length (sd-) and medium chain length (mcl-)PHAs can be synthesized by different bacteria and these polymers can be depolymerized to C4 to C6, and 08, ClO, and
012 3-hydroxy fatty acids that can be converted to methyl esters, methyl ethers, alkanes, and/or alkenes, which will be evaluated as potential fuel additives for biodiesel, or converted to hydrocarbons to make “green-gasoline” or bio-jet fuels.

Insights and Recommendation Extraction using advances in Large Language Modeling

Businesses allocate a significant amount of financial resources and time to produce regular financial and managerial reports,
which involve analyzing accounting data. These reports play a vital role in evaluating and comprehending the overall
performance of the business, thereby aiding in making important decisions. Additionally, businesses often engage industry
experts or consultants to derive actionable recommendations and strategies based on the findings from the analysis. The
ultimate goal is to deliver these recommendations in a clear and easily understandable format, typically using a SaaS model
application. Automating this entire process comes with several benefits, such as enhancing operational efficiency, reducing
the likelihood of manual errors, and ultimately leading to cost savings. This project is specifically focused on extracting and
generating meaningful business recommendations using interpretable Large Language Models (LLMs). These LLMs are
sophisticated language models that can process and understand vast amounts of data. Ensuring the accuracy and
effectiveness of these generated recommendations is critical to maintaining the quality of the automatically generated
reports. The project aims to revolutionize the way businesses perform analysis and make decisions. It has the potential to
transform the current landscape of analysis and decision-making processes, making them more data-driven, efficient, and
precise.

Towards a theory of territorial justice

The research project aims to study how the current dynamics of spatial production determine the pattern of global inequality, how the urban “way of life” is shaping the appropriation of natural spaces and what elements can configure a theory of justice that contributes to a positive redefinition of territorial policies. Faced with a context and, above all, the perspective of a future dominated by logics that produce and reproduce territorial inequalities, the central question that this research seeks to explore is what it means to build just cities in the context of planetary urbanization. To this objective, it builds on the expansion of the traditional idea of urbanization as the growth of cities towards a more complex and holistic concept as the process of territorial restructuring.

Evaluating Learning Rules in Visual Perceptual Learning Using Deep Neural Networks

This research project aims to explore and outline the implications of supervised and reinforcement learning dynamics in Deep Neural Networks (DNNs) with regard to Visual Perceptual Learning (VPL). DNNs, which are hierarchical computational models inspired by the biological brain, will be used to simulate various VPL tasks. The focus is on assessing how these distinct learning approaches—supervised, where networks are explicitly trained with correct responses, and reinforcement, where learning occurs via trial and error feedback—affect task performance and learning transferability. These effects will be evaluated by comparing the DNN outcomes with known human and animal perceptual learning characteristics The ultimate goal is to enhance our understanding of the underlying mechanisms of perceptual learning and to determine which learning paradigm most accurately replicates biological processes. This study promises to contribute significantly to our theoretical understanding of VPL and provide valuable insights into the design of more efficient, biologically-inspired artificial learning systems.

Conception d’un capteur microfluidique 3D à résonateur micro-ondes pour les liquides

Le présent stage de recherche a pour objectif d’explorer la conception et l’étude d’un capteur microfluidique à résonateur micro-ondes dans le but de caractériser et de détecter des liquides. Ce stage s’inscrit dans le cadre d’un projet de recherche doctoral mené par le stagiaire à l’Université du Québec à Trois-Rivières (UQTR), en collaboration avec une équipe de recherche spécialisée dans les technologies de micro-ondes de l’Université Grenoble Alpes (UGA), en France.

Le stagiaire aura pour mission de développer une nouvelle structure de capteur microfluidique à résonateur micro-ondes en utilisant la technologie CSRR (complementary split-ring resonator) et le mode différentiel. Cette tâche impliquera l’utilisation de logiciels de simulation de micro-ondes tels que HFSS et ADS, ainsi que de nouveaux outils de l’UGA pour concevoir et optimiser le capteur.

La fabrication du capteur sera effectuée en collaboration avec le Centre National Intégré du Manufacturier Intelligent (CNIMI) en utilisant l’imprimante 3D DragonFly. Le stagiaire sera chargé de réaliser des tests expérimentaux et d’évaluer les performances du capteur en utilisant les équipements de l’équipe de recherche de l’UGA, tels que l’analyseur de réseau vectoriel (VNA).